Wireless sensor networks are comprised of a plurality of spatially distributed autonomous sensing devices which cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, field strength, pressure, motion, or pollutants at different locations. Each sensor is configured to collect, process, and store environmental information as well as communicate with other sensors via intersensor communication. In many scenarios, such as urban rescue and battlefield surveillance, a pre-deployed static sensor network is either costly or difficult to implement. Mobile nodes provide a much more flexible and practical deployment approach. Also, some applications (e.g., non-intrusive habitat study) prohibit the deployment of high density networks, but are well suited to the use of mobile nodes.
The rapid development of wireless communications and embedded microsensing technologies has facilitated the use of mobile sensor networks in our daily lives. A wide range of applications exist for mobile sensor networks, including environmental monitoring, health care, nuclear, biological, and chemical attack detection, intruder detection, field measurements, remote sensing, object tracking, unmanned ground or aerial vehicles (UGVs and UAVs), sensors mounted on vehicles such as taxis, busses, trucks, fleets, or passenger cars, as well as sensors carried by humans including handheld cameras, mobile telephones, smartphones, and GPS devices. Using wireless communication technology, it is possible to create a network of these mobile sensor platforms so that even if the individual sensors are error-prone and limited in their range, when utilized together as part of a network, the sensors are able to cover a wide geographical area and provide accurate sensing of the environment.
In many mobile sensor platforms, the number of available sensing resources is suboptimal. More specifically, the number of available sensing devices, as well as the available wireless bandwidth, are both less than what is optimally needed to implement various ongoing tasks or missions. In these situations, the problem of resource allocation naturally arises wherein one potential solution is to dedicate certain sensing resources, such as sensors or sensor platforms, to a particular mission or task so that the overall allocation of sensing resources to the missions maximizes a utility function.
Currently, sensor-enabled applications assume a rather static relationship between applications and sensor platforms. Either the applications are statically bound to their sensors such that applications and sensors are deployed which are already bound to one another, or the sensors are static, or both. In both situations, the sensors are dedicated to a “region of interest” which the sensors monitor on behalf of applications for a prescribed period of time. This prescribed period of time could be infinite, i.e., forever. These sensors monitor for events and respond to their applications whenever events of interest occur. Current sensor systems cannot adequately cope with mobile operational environments which, because of their mobile nature, may cover large areas and thus require deployment of an undesirably large number of sensors.